The encoding aperture snapshot spectral imaging system， based on compressive sensing theory， can be regarded as an encoder， which can efficiently obtain compressed two-dimensional spectral data and then decode it into three-dimensional spectral data through deep neural networks. However， training the deep neural networks requires a large amount of clean data that is difficult to obtain. To address the problem of insufficient training data for deep neural networks， a self-supervised hyperspectral denoising neural network based on neighborhood sampling is proposed. This network is integrated into a deep plug-and-play framework to achieve self -supervised spectral reconstruction. The study also examines the impact of different noise degradation models on the final reconstruction quality. Experimental results demonstrate that self-supervised learning method enhances the average peak signal-to-noise ratio by 1.18 dB and improves the structural similarity by 0.009 compared with the supervised learning method. Additionally， it achieves better visual reconstruction results.